Abstract

This paper introduces simulated protozoa optimization (SPO). SPO is a multi-agent heuristic technique that models the foraging and reproductive behavior of unicellular organisms such as Paramecium caudatum. In one set of experiments, SPO-based algorithms were used to solve a set of five standard benchmark numeric minimization problems including the Rastrigin function and the Schwefel function. Compared to the related techniques particle swarm optimization (PSO), bacterial foraging optimization (BFO), and genetic algorithm optimization (GAO), SPO produced better results in terms of both solution accuracy and performance. In a second set of experiments, when used as the weight and bias estimation mechanism for neural network classification, SPO produced better accuracy than PSO, BFO and GAO. An analysis of SPO algorithms indicates that the two most important factors contributing to SPO effectiveness are those that model protozoan fission and conjugation. The results suggest that SPO is a promising new optimization technique that may be particularly applicable to the analysis of very large data sets.

Details

Publication type

Proceedings

Publisher

Proceedings of the 13th Annual IEEE International Conference on Information Reuse and Integration